Classifying reanalysis surface temperature probability density functions (PDFs) over North America with cluster analysis

Abstract

[1] An important step in projecting future climate change impacts on extremes involves quantifying the underlying probability distribution functions (PDFs) of climate variables. However, doing so can prove challenging when multiple models and large domains are considered. Here an approach to PDF quantification using k-means clustering is considered. A standard clustering algorithm (with k = 5 clusters) is applied to 33 years of daily January surface temperature from two state-of-the-art reanalysis products, the North American Regional Reanalysis and the Modern Era Retrospective Analysis for Research and Applications. The resulting cluster assignments yield spatially coherent patterns that can be broadly related to distinct climate regimes over North America, e.g., low variability over the tropical oceans or temperature advection across stronger or weaker gradients. This technique has the potential to be a useful and intuitive tool for evaluation of model-simulated PDF structure and could provide insight into projections of future changes in temperature. Citation: Loikith, P. C., B. R. Lintner, J. Kim, H. Lee, J. D. Neelin, and D. E. Waliser (2013), Classifying reanalysis surface temperature probability density functions (PDFs) over North America with cluster analysis,

Northern Hemisphere Climatology and Trends of Statistical Moments Documented from GHCN-Daily Surface Air Temperature Station Data from 1950 to 2010

“…Loikith and Broccoli (2012) also suggest that regional variations in skewness may point to regions whose temperature extremes are dominated by local or regional circulation dynamics. During DJF, when semipermanent high and low pressure systems are strongest, geographical skew patterns are coherent on synoptic scales, suggesting that DJF extremes are dominated by large-scale dynamic regimes, consistent with Loikith and Broccoli (2012).…”

Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies

“…The process of evaluating climate models using datasets from multiple sources and modularized OCW libraries. The evaluation metrics and plots in Kim et al (2013) are shown as an example.…”

“…RCMES provides basic metrics, such as bias calculation, Taylor diagram, and comparison of time series. Earlier RCMES publications (Kim et al, 2013(Kim et al, , 2014 show how to use the basic metrics in multi-model evaluation as illustrated in Figure 4. The metrics module also provides more advanced metrics.…”

“…In CFiles, users can define the evaluation domain, the time period of evaluation, regridding options, and performance metrics to calculate. Released RCMES packages include example CFiles to 5 reproduce the plots/diagrams in the four selected peer-reviewed journal articles including (Kim et al, 2013(Kim et al, , 2014. Kim et al (2013) and Kim et al (2014) evaluate RCM simulations over the North America and Africa respectively.…”

“…Released RCMES packages include example CFiles to 5 reproduce the plots/diagrams in the four selected peer-reviewed journal articles including (Kim et al, 2013(Kim et al, , 2014. Kim et al (2013) and Kim et al (2014) evaluate RCM simulations over the North America and Africa respectively. The tutorials on the RCMES websites (JPL, 2018b) provide step-by-step instructions to reproduce all of the figures included in the two published articles.…”